2026-07-22T03:37:57Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/263772025-12-18T12:55:57Zcom_20.500.12105_19586com_20.500.12105_2202col_20.500.12105_19587
Repisalud author Andrés Bell-Navas author Nourelhouda Groun author María Villalba-Orero author Enrique Lara-Pezzi author Jesús Garicano-Mena author Soledad Le Clainche funder Ministerio de Ciencia e Innovación (España) funder Unión Europea. Comisión Europea. NextGenerationEU funder Comunidad de Madrid (España) funder Ministerio de Ciencia, Innovación y Universidades (España) funder Instituto de Salud Carlos III 2025-02-26T10:50:31Z 2025-02-26T10:50:31Z 2025-03 Expert Syst Appl. 2025 Mar 10; 264: 125849. 10.1016/j.eswa.2024.125849 0957-4174 EXPERT SYSTEMS WITH APPLICATIONS https://hdl.handle.net/20.500.12105/26377 Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering the increase of medical data, much pressure is put on the health industry to develop systems for early and accurate heart disease recognition. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed, which analyses in real-time echocardiography video sequences. The system works in two stages. The first one transforms the data included in a database of echocardiography sequences into a machine-learning-compatible collection of annotated images which can be used in the training stage of any kind of machine learning-based framework, and more specifically with deep learning. This includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first time to the authors’ knowledge, for both data augmentation and feature extraction in the medical field. The second stage is focused on building and training a Vision Transformer (ViT), barely explored in the related literature. The ViT is adapted for an effective training from scratch, even with small datasets. The designed neural network analyses images from an echocardiography sequence to predict the heart state. The results obtained show the superiority of the proposed system and the efficacy of the HODMD algorithm, even outperforming pretrained Convolutional Neural Networks (CNNs), which are so far the method of choice in the literature. eng Automatic Cardiac Pathology Recognition in Echocardiography Images using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets research article 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 URL https://repisalud.isciii.es/bitstreams/bd839f2e-358e-4cd0-a2b8-b3e651e2cbd3/download File MD5 ec3fb1bae408ce937807843d08555732 955638 application/pdf Automatic Cardiac Pathology_Expert Syst Appl_2025.pdf